CalibCombineTask¶

class lsst.cp.pipe.CalibCombineTask(**kwargs)¶

Bases: lsst.pipe.base.PipelineTask, lsst.pipe.base.CmdLineTask

Task to combine calib exposures.

Attributes Summary

canMultiprocess

Methods Summary

`applyOverrides`(config)	A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.
`applyScale`(exposure[, scale])	Apply scale to input exposure.
`combine`(target, expHandleList, expScaleList, …)	Combine multiple images.
`combineHeaders`(expHandleList, calib[, …])	Combine input headers to determine the set of common headers, supplemented by calibration inputs.
`emptyMetadata`()	Empty (clear) the metadata for this Task and all sub-Tasks.
`getAllSchemaCatalogs`()	Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.
`getDimensions`(expHandleList)	Get dimensions of the inputs.
`getFullMetadata`()	Get metadata for all tasks.
`getFullName`()	Get the task name as a hierarchical name including parent task names.
`getName`()	Get the name of the task.
`getResourceConfig`()	Return resource configuration for this task.
`getSchemaCatalogs`()	Get the schemas generated by this task.
`getSize`(dimList)	Determine a consistent size, given a list of image sizes.
`getTaskDict`()	Get a dictionary of all tasks as a shallow copy.
`interpolateNans`(exp)	Interpolate over NANs in the combined image.
`makeField`(doc)	Make a `lsst.pex.config.ConfigurableField` for this task.
`makeSubtask`(name, **keyArgs)	Create a subtask as a new instance as the `name` attribute of this task.
`parseAndRun`([args, config, log, doReturnResults])	Parse an argument list and run the command.
`run`(inputExpHandles[, inputScales, inputDims])	Combine calib exposures for a single detector.
`runQuantum`(butlerQC, inputRefs, outputRefs)	Method to do butler IO and or transforms to provide in memory objects for tasks run method
`timer`(name, logLevel)	Context manager to log performance data for an arbitrary block of code.
`writeConfig`(butler[, clobber, doBackup])	Write the configuration used for processing the data, or check that an existing one is equal to the new one if present.
`writeMetadata`(dataRef)	Write the metadata produced from processing the data.
`writePackageVersions`(butler[, clobber, …])	Compare and write package versions.
`writeSchemas`(butler[, clobber, doBackup])	Write the schemas returned by `lsst.pipe.base.Task.getAllSchemaCatalogs`.

Attributes Documentation

canMultiprocess = True¶

Methods Documentation

classmethod applyOverrides(config)¶

A hook to allow a task to change the values of its config after the camera-specific overrides are loaded but before any command-line overrides are applied.

Parameters:	config : instance of task’s `ConfigClass` Task configuration.

Notes

This is necessary in some cases because the camera-specific overrides may retarget subtasks, wiping out changes made in ConfigClass.setDefaults. See LSST Trac ticket #2282 for more discussion.

Warning

This is called by CmdLineTask.parseAndRun; other ways of constructing a config will not apply these overrides.

applyScale(exposure, scale=None)¶

Apply scale to input exposure.

This implementation applies a flux scaling: the input exposure is divided by the provided scale.

Parameters:	exposure : `lsst.afw.image.Exposure` Exposure to scale. scale : `float` or `list` [`float`], optional Constant scale to divide the exposure by.

combine(target, expHandleList, expScaleList, stats)¶

Combine multiple images.

Parameters:	target : `lsst.afw.image.Exposure` Output exposure to construct. expHandleList : `list` [`lsst.daf.butler.DeferredDatasetHandle`] Input exposure handles to combine. expScaleList : `list` [`float`] List of scales to apply to each input image. stats : `lsst.afw.math.StatisticsControl` Control explaining how to combine the input images.

combineHeaders(expHandleList, calib, calibType='CALIB', scales=None)¶

Combine input headers to determine the set of common headers, supplemented by calibration inputs.

Parameters:	expHandleList : `list` [`lsst.daf.butler.DeferredDatasetHandle`] Input list of exposure handles to combine. calib : `lsst.afw.image.Exposure` Output calibration to construct headers for. calibType : `str`, optional OBSTYPE the output should claim. scales : `list` [`float`], optional Scale values applied to each input to record.
Returns:	header : `lsst.daf.base.PropertyList` Constructed header.

emptyMetadata() → None¶: Empty (clear) the metadata for this Task and all sub-Tasks.

getAllSchemaCatalogs() → Dict[str, Any]¶

Get schema catalogs for all tasks in the hierarchy, combining the results into a single dict.

Returns:	schemacatalogs : `dict` Keys are butler dataset type, values are a empty catalog (an instance of the appropriate `lsst.afw.table` Catalog type) for all tasks in the hierarchy, from the top-level task down through all subtasks.

Notes

This method may be called on any task in the hierarchy; it will return the same answer, regardless.

The default implementation should always suffice. If your subtask uses schemas the override Task.getSchemaCatalogs, not this method.

getDimensions(expHandleList)¶

Get dimensions of the inputs.

Parameters:	expHandleList : `list` [`lsst.daf.butler.DeferredDatasetHandle`] Exposure handles to check the sizes of.
Returns:	width, height : `int` Unique set of input dimensions.

getFullMetadata() → lsst.pipe.base._task_metadata.TaskMetadata¶

Get metadata for all tasks.

Returns:	metadata : `TaskMetadata` The keys are the full task name. Values are metadata for the top-level task and all subtasks, sub-subtasks, etc.

Notes

The returned metadata includes timing information (if @timer.timeMethod is used) and any metadata set by the task. The name of each item consists of the full task name with . replaced by :, followed by . and the name of the item, e.g.:

topLevelTaskName:subtaskName:subsubtaskName.itemName

using : in the full task name disambiguates the rare situation that a task has a subtask and a metadata item with the same name.

getFullName() → str¶

Get the task name as a hierarchical name including parent task names.

Returns:	fullName : `str` The full name consists of the name of the parent task and each subtask separated by periods. For example: The full name of top-level task “top” is simply “top”. The full name of subtask “sub” of top-level task “top” is “top.sub”. The full name of subtask “sub2” of subtask “sub” of top-level task “top” is “top.sub.sub2”.

getName() → str¶

Get the name of the task.

Returns:	taskName : `str` Name of the task.

See also

Task.getAllSchemaCatalogs

Notes

Warning

Subclasses that use schemas must override this method. The default implementation returns an empty dict.

This method may be called at any time after the Task is constructed, which means that all task schemas should be computed at construction time, not when data is actually processed. This reflects the philosophy that the schema should not depend on the data.

Returning catalogs rather than just schemas allows us to save e.g. slots for SourceCatalog as well.

getSize(dimList)¶

Determine a consistent size, given a list of image sizes.

Parameters:	dimList : `list` [`tuple` [`int`, `int`]] List of dimensions.
Returns:	width, height : `int` Common dimensions.
Raises:	RuntimeError If input dimensions are inconsistent.

getTaskDict() → Dict[str, weakref]¶

Get a dictionary of all tasks as a shallow copy.

Returns:	taskDict : `dict` Dictionary containing full task name: task object for the top-level task and all subtasks, sub-subtasks, etc.

interpolateNans(exp)¶

Interpolate over NANs in the combined image.

NANs can result from masked areas on the CCD. We don’t want them getting into our science images, so we replace them with the median of the image.

Parameters:	exp : `lsst.afw.image.Exposure` Exp to check for NaNs.

classmethod makeField(doc: str) → lsst.pex.config.configurableField.ConfigurableField¶

Make a lsst.pex.config.ConfigurableField for this task.

Parameters:	doc : `str` Help text for the field.
Returns:	configurableField : `lsst.pex.config.ConfigurableField` A `ConfigurableField` for this task.

Examples

Provides a convenient way to specify this task is a subtask of another task.

Here is an example of use:

class OtherTaskConfig(lsst.pex.config.Config):
    aSubtask = ATaskClass.makeField("brief description of task")

makeSubtask(name: str, **keyArgs) → None¶

Create a subtask as a new instance as the name attribute of this task.

Parameters:	name : `str` Brief name of the subtask. keyArgs Extra keyword arguments used to construct the task. The following arguments are automatically provided and cannot be overridden: “config”. “parentTask”.

Notes

The subtask must be defined by Task.config.name, an instance of ConfigurableField or RegistryField.

classmethod parseAndRun(args=None, config=None, log=None, doReturnResults=False)¶

Parse an argument list and run the command.

Parameters:

args : list, optional: List of command-line arguments; if None use sys.argv.
config : lsst.pex.config.Config-type, optional: Config for task. If None use Task.ConfigClass.
log : logging.Logger-type, optional: Log. If None use the default log.
doReturnResults : bool, optional: If True, return the results of this task. Default is False. This is only intended for unit tests and similar use. It can easily exhaust memory (if the task returns enough data and you call it enough times) and it will fail when using multiprocessing if the returned data cannot be pickled.

Returns:

struct : lsst.pipe.base.Struct

Fields are:

argumentParser: the argument parser (lsst.pipe.base.ArgumentParser).
parsedCmd: the parsed command returned by the argument parser’s parse_args method (argparse.Namespace).
taskRunner: the task runner used to run the task (an instance of Task.RunnerClass).
resultList: results returned by the task runner’s run method, one entry per invocation (list). This will typically be a list of Struct, each containing at least an exitStatus integer (0 or 1); see Task.RunnerClass (TaskRunner by default) for more details.

Notes

Calling this method with no arguments specified is the standard way to run a command-line task from the command-line. For an example see pipe_tasks bin/makeSkyMap.py or almost any other file in that directory.

If one or more of the dataIds fails then this routine will exit (with a status giving the number of failed dataIds) rather than returning this struct; this behaviour can be overridden by specifying the --noExit command-line option.

run(inputExpHandles, inputScales=None, inputDims=None)¶

Combine calib exposures for a single detector.

Parameters:	inputExpHandles : `list` [`lsst.daf.butler.DeferredDatasetHandle`] Input list of exposure handles to combine. inputScales : `dict` [`dict` [`dict` [`float`]]], optional Dictionary of scales, indexed by detector (`int`), amplifier (`int`), and exposure (`int`). Used for ‘inputExps’ scaling. inputDims : `list` [`dict`] List of dictionaries of input data dimensions/values. Each list entry should contain: `"exposure"` exposure id value (`int`) `"detector"` detector id value (`int`)
Returns:	results : `lsst.pipe.base.Struct` The results struct containing: `outputData` Final combined exposure generated from the inputs (`lsst.afw.image.Exposure`).
Raises:	RuntimeError Raised if no input data is found. Also raised if config.exposureScaling == InputList, and a necessary scale was not found.

runQuantum(butlerQC, inputRefs, outputRefs)¶

Method to do butler IO and or transforms to provide in memory objects for tasks run method

Parameters:

butlerQC : ButlerQuantumContext: A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.
inputRefs : InputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.
outputRefs : OutputQuantizedConnection: Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.

timer(name: str, logLevel: int = 10) → Iterator[None]¶

Context manager to log performance data for an arbitrary block of code.

Parameters:	name : `str` Name of code being timed; data will be logged using item name: `Start` and `End`. logLevel A `logging` level constant.

Navigation

CalibCombineTask¶